I'm no expert in this space, but you might try setting a max time on the
local optimizer `lopt` as well.
As I understand it, AUGLAG works by running the local optimizer several
times with higher and higher penalties on the constraints. If you give the
local optimizer infinite time and it's not converging, it will take up all
of the time allotted to the outer optimizer without triggering additional
outer loops in `gopt`.
You might also try adjusting the precision of `lopt` for the same reasons.
--
Jake Biesinger
On Tue, Apr 26, 2016 at 6:07 AM, David Morris <otha...@othalan.net> wrote:
> UPDATE:
>
> I just found out that this seems related to setting the time limit.
>
> If I comment out the line "gopt.set_maxtime(8.0)", optimization works as
> expected.
>
> Setting a maximum time limit is necessary as it is possible a solution
> will not be reached in an acceptable amount of time.
>
> Is this a bug?
>
> Thank you,
>
> David
>
>
> On Tue, Apr 26, 2016 at 7:57 PM, David Morris <otha...@othalan.net> wrote:
>
>> I am using NLOpt in a python application and am having problems when
>> setting an equality constraint when using LN_AUGLAG.
>>
>> If I use LN_COBYLA, optimization works perfectly. However, if I use
>> LN_AUGLAG and set LN_COBYLA as the local optimizer, the result is the exact
>> same as my initial guess. My goal is to use experiment with other
>> optimization algorithms (for example, LN_SBPLX), and use AUGLAG to provide
>> the quality constraint.
>>
>> Can anyone help determine why the equality constraint causes AUGLAG to
>> fail?
>>
>> Below is sample code showing how I use NLOpt.
>>
>>
>>
>> ###################################################################################
>> # CODE Sample:
>>
>> args = ( ... )
>> kw = { ... }
>>
>> # Optimization Function for NLOpt
>> def optfunc(x,grad):
>> # Big complex function which calculates a single return value:
>> val = model_p_mixer(x,*args,**kw)
>> return val
>>
>> # Constraint Function for NLOpt
>> # x -> percentage of total content for each component
>> # sum(x) == 100 %
>> def opt_constraint(x, grad):
>> val = float(100.0 - x.sum())
>> return val
>>
>> gopt = nlopt.opt(nlopt.LN_AUGLAG, len(guess))
>> lopt = nlopt.opt(nlopt.LN_COBYLA, len(guess))
>>
>> gopt.set_min_objective(optfunc)
>>
>> gopt.set_lower_bounds([98.62, 0.0, 0.0])
>> gopt.set_upper_bounds([99.5, 1.0, 1.0])
>>
>> gopt.add_equality_constraint(opt_constraint, 0.001)
>>
>> # Set tolerances to determine when the optimizer stops looking
>> for solutions
>> gopt.set_xtol_abs(1E-6)
>> gopt.set_ftol_abs(0.001)
>> lopt.set_xtol_abs(1E-6)
>> lopt.set_ftol_abs(0.001)
>>
>> # Set initial step size
>> gopt.set_initial_step(0.01)
>>
>> gopt.set_maxtime(8.0)
>>
>> gopt.set_local_optimizer(lopt)
>>
>> # Run the optimizer
>> mix = gopt.optimize([99.0, 0.5, 0.5])
>>
>> # Initial Guess : [99.0, 0.5 , 0.5 ]
>> # Expected Result: [99.2, 0.35, 0.45]
>> # Actual Result: [99.0, 0.5 , 0.5 ]
>>
>> ###################################################################################
>>
>>
>> Thank you,
>>
>> David
>>
>
>
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